GB/T 46019.1-2025Plastics — Identification of recycled plastics — Part 1: Poly(ethylene terephthalate) (PET) materials (English PDF)

3.1 post-consumer plastic

Plastic generated by end users that has fulfilled its intended purpose or can no longer be used (including plastic returned from the distribution chain).

[Source. GB/T 30102-2024, 3.3, modified]

3.2 chemical recycling

Process in which the chemical structure of plastic waste is altered through reactions such as pyrolysis, gasification or depolymerization, excluding energy recovery and incineration, thereby generating new monomers or raw materials.

Note. This includes adding recycled polymers directly to the virgin polymer polymerization reaction system, and preparing new materials containing recycled components through partial depolymerization and repolymerization processes.

[Source. GB/T 45090-2024, 3.4, modified]

3.3 random forest model

A machine learning method that performs classification or regression by constructing multiple decision trees and aggregating their predictions.

3.4 gini impurity

Measure of the purity of a dataset, representing the probability that two samples randomly selected from the dataset belong to different categories.

Note. The smaller the gini impurity, the higher the purity of the dataset.

3.5 grid search

Systematic hyperparameter optimization method that determines the optimal combination of hyperparameters by training models and evaluating their performance for each combination within a predefined hyperparameter search space.

3.6 receiver operating characteristic curve; ROC

Performance evaluation method in which the false positive rate and the true positive rate are calculated at different classification thresholds and their relationship is plotted as a curve to assess the ability of a classification model to distinguish between positive and negative samples.

Note. The area under the receiver operating characteristic curve (ROC-AUC) can be used as a quantitative indicator of the overall discriminative ability of the model. The closer the value is to 1, the better the performance.

3.7 precision

Proportion of samples predicted as positive by a model that are actually positive.

3.8 recall

Proportion of actual positive samples that are correctly identified as positive by a model.

5.1 Sample collection

To ensure the stability and applicability of the model, collect a sufficient number of representative virgin PET and recycled PET samples for model construction. There shall be no fewer than 75 batches of virgin PET and recycled PET samples, covering different sources, manufacturing techniques, and manufacturers. To avoid imbalanced sample distribution, the proportion of either category (virgin PET or recycled PET) in all samples shall not exceed 60%.

5.2 Sample analysis

5.2.1 Reagents and materials

5.2.1.1 Standards

5.2.1.1.1 Mixed standard solution of n-alkanes (C7-C28, a total of 22 kinds), with n- hexane as the solvent; the mass concentration of each substance shall be 1 000 µg/mL.

5.2.1.1.2 D8-naphthalene (CAS No.. 1146-65-2), purity >= 99.5% (mass fraction).

5.2.1.2 Reagents

5.2.1.2.1 Absolute ethanol. chromatographic pure.

5.2.1.2.2 Liquid nitrogen. purity 99.999% (volume fraction).

5.2.2 Instruments and apparatuses

5.2.2.1 Headspace gas chromatography-mass spectrometry system. equipped with an

EI source, operating temperature of the headspace sampler not lower than 200 °C.

5.2.2.2 Headspace bottle. 20 mL, provided with aluminum cap and PTFE silicone

rubber gasket.

5.2.2.3 Dewar flask. equipped with a sealing cap.

5.2.2.4 High-speed rotary crusher. rotation speed not less than 10 000 r/min, equipped

with a screen with an aperture not greater than 0.5 mm.

5.2.2.5 Analytical balance. sensitivity 0.01 mg.

5.2.2.6 Analytical balance. sensitivity 0.1 mg.

5.2.3 Solution preparation

5.2.3.1 D8-naphthalene stock solution (1 000 µg/mL)

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